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Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications de Laura Igual

Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications: Undergraduate Topics in Computer Science

Autor Laura Igual, Santi Seguí Contribuţii de Jordi Vitrià, Eloi Puertas, Petia Radeva, Oriol Pujol, Sergio Escalera, Francesc Dantí, Lluís Garrido
en Limba Engleză Paperback – 2 mar 2017
This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.

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Specificații

ISBN-13: 9783319500164
ISBN-10: 3319500163
Pagini: 240
Ilustrații: XIV, 218 p. 73 illus., 67 illus. in color.
Dimensiuni: 155 x 235 x 15 mm
Greutate: 0.4 kg
Ediția:2017
Editura: Springer International Publishing
Colecția Springer
Seria Undergraduate Topics in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Introduction to Data Science.- Toolboxes for Data Scientists.- Descriptive statistics.- Statistical Inference.- Supervised Learning.- Regression Analysis.- Unsupervised Learning.- Network Analysis.- Recommender Systems.- Statistical Natural Language Processing for Sentiment Analysis.- Parallel Computing.

Recenzii

“This book contains a broad range of timely topics and presents interesting examples on real-life data using Python. … the book is a good addition to references on Python and data science. Students as well as practicing data scientists and engineers will benefit from the many techniques and use cases presented in the book.” (Computing Reviews, December, 2017)
“The book ‘Introduction to Data Science’ is built as a starter presentation of concepts, techniques and approaches that constitute the initial contact with data science for scientists … . The style of the book recommends it to both undergraduates and postgraduates and the concluding remarks and references provide guidance for the next steps in the study of particular topics.” (Irina Ioana Mohorianu, zbMATH, Vol. 1365.62003, 2017)

Notă biografică

Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain. Dr. Santi Seguí is an Assistant Professor at the same institution.
The authors wish to mention that some chapters were co-written by Jordi Vitrià, Eloi Puertas, Petia Radeva, Oriol Pujol, Sergio Escalera, Francesc Dantí and Lluís Garrido.   

Textul de pe ultima copertă

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis.
Topics and features:
  • Provides numerous practical case studies using real-world data throughout the book
  • Supports understanding through hands-on experience of solving data science problems using Python
  • Describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming
  • Reviews a range of applications of data science, including recommender systems and sentiment analysis of text data
  • Provides supplementary code resources and data at an associated website
This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses.
Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain. Dr. Santi Seguí is an Assistant Professor at the same institution.

Caracteristici

Describes tools and techniques that demystify data science Presents a focus on analytical techniques; the core toolbox for every data scientist Includes numerous practical case studies using real-world data, supplying code examples and data at an associated website Discusses Python extensions, techniques and modules to perform statistical analysis and machine learning, and important applications of data science Includes supplementary material: sn.pub/extras